
Technically correct: As stated in [1]} and also in our recent paper [2]}, slow drift in EEG can inflate the performance of automated classifiers.

Technically wrong:
The claim in [1]} that classification accuracy in block-design experiments is only due to temporal correlation in EEG signals is incorrect.
Instead, it appears that the data reported in [1]} is highly affected by temporal correlation, due to incorrect experimental protocols, including procedures that maximize the purported temporal correlation and perform per-subject classification.
However, given the small scale of our and their experiments, the correctness or incorrectness of what we (and they) found needs to be further examined.

Definitely wrong: We believe that an investigation of the effect of temporal correlations on EEG machine learning classification performance (including possible errors) is part of the natural scientific process that a new — and immature — interdisciplinary area must go through.
However, misinterpreting results, making untrue claims, reporting unverified opinions, and making malicious insinuationsThe pre-print of [1]}, available in , sports a malicious title (which is repeated several times in the introduction and conclusion) — “Training on the test set?” — with a reference to [6]}, which could only cause the reader to question the credibility of the authors of the cited works. We all know how serious an accusation of “training on the test set” would be for the machine learning community. Even assuming, for the sake of argument, that there was an inadvertent bias in [6]}'s data, that is technically and fundamentally different from ``training on the test set”. This behavior is reiterated in the abstract of [1]}, stating that our block design leads to “surreptitiously” training on the test set. is unacceptable.
This has nothing to do with scientific progress and makes [1]}'s efforts appear to be oriented to hurt the reputation of the authors of [6]} rather than help to progress the field.

